An essential part of any forward looking sonar system is the ability to successfully utilize the data presented by the system. Typically, beamformed data is used to determine the location of objects within a volume of fluid, hereby defined as targets. Traditional beamformed data can be ambiguous to the interpreter be it a human or another system, due to it's complexity and/or the existence of various sources of noise within the data such as surface noise and sidelobes. Thus, a need exists to extract the essential target information from a set of beamformed data while eliminating sources of noise. Traditional display systems have tried to simplify the display by simply finding and displaying a single point within each beam of the beamformed data set. These systems are often called profiling sonars. Common single point detection routines extract these points by finding the “critical” bin along each beam. This critical bin may be defined in many ways. Two common definitions are: 1. the bin which contains the strongest signal within a beam, or 2. the first bin within a beam when moving away from the sonar point of origin to contain a signal above a specified threshold. This approach can be moderately effective in simple environments using only 2-dimensional beamformed data. This approach does not utilize any correlation techniques and each bin, target, and beam is evaluated independently of every other bin, target and beam. This approach is often lacking in image quality for complicated or 3-dimensional beamformed data sets. A better way of extracting data from a 3-dimensional beamformed data set, particularly for use in forward-looking applications, and a way which can improve image quality through correlation techniques is desirable.